SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks

TL;DR

This work addresses the lack of real-world benchmarks for applying graph neural networks to supply chain planning by introducing SupplyGraph, a temporally rich graph dataset sourced from a major FMCG company in Bangladesh. Nodes represent products and edges encode group, plant, and storage relationships, with node features capturing production, sales orders, distributor deliveries, and factory issues over 2023-01-01 to 2023-08-09. The dataset supports a range of graph-based tasks including demand forecasting, production planning, anomaly detection, and edge/relationship prediction, and provides detailed statistics across product groups, sub-groups, plants, and storage locations. By presenting both homogeneous and potential heterogeneous/hypergraph formulations, the paper emphasizes the dataset’s utility for benchmarking GNN methods and advancing supply chain analytics and planning in practice.

Abstract

Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graph-like in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problems using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of factory issues. By utilizing this dataset, researchers can employ GNNs to address numerous supply chain problems, thereby advancing the field of supply chain analytics and planning. Source: https://github.com/CIOL-SUST/SupplyGraph

Figures (8)

• Figure 1: Supply Chain Problem Formulation in Homogeneous Graph. Boxes represent various product types, with color indicating different groups. They are closely located based on product groups and production facilities. Different relational connections denote shared raw material requirements, interdependence between products, and other impacts.
• Figure 2: Homogeneous Graphs from the dataset. (a) Nodes are sub-group products, and plants are edges. (b) Nodes plant-products and storage locations are edges. (c) Nodes are sub-group products and storage locations are edges.
• Figure 3: Product group count.
• Figure 4: Product subgroup count.
• Figure 5: All product's production (MT) of POV sub-group.